Measurement of Diaphragmatic Electrical Activity by Surface Electromyography in Intubated Subjects and Its Relationship With Inspiratory Effort
Bibliographic record
Abstract
BACKGROUND: Quantification of patient effort during spontaneous breathing is important to tailor ventilatory assistance. Because a correlation between inspiratory muscle pressure (P mus ) and electrical activity of the diaphragm (EA di ) has been described, we aimed to assess the reliability of surface electromyography (EMG) of the respiratory muscles for monitoring diaphragm electrical activity and subject effort during assisted ventilation. METHODS: At a general ICU of a single university-affiliated hospital, we enrolled subjects who were intubated and on pressure support ventilation (PSV) and were on mechanical ventilation for > 48 h. The subjects were studied at 3 levels of pressure support. Airway flow and pressure; esophageal pressure; EA di ; and surface EMG of the diaphragm (surface EA di ), intercostal, and sternocleidomastoid muscles were recorded. Respiratory cycles were sampled for off-line analysis. The P mus /EA di index (PEI) was calculated by relying on EA di and surface EA di (surface PEI) from an airway pressure drop during end-expiratory occlusions performed every minute. RESULTS: surface EA di well correlated with EA di and P mus , in particular, after averaging breaths into deciles (R = 0.92 and R = 0.84). When surface PEI was used with surface EA di , it provided a reliable estimation of P mus (R = 0.94 in comparison with measured P mus ). CONCLUSIONS: During assisted mechanical ventilation, EA di can be reliably monitored by both EA di and surface EMG. The measurement of P mus based on the calibration of EA di was also feasible by the use of surface EMG.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".